An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused
on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower
thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a
deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation
method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines
the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge
on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained
from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component
vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image
by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by
using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical
area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939
mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation
algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar
We present a novel view on 2D/3D image registration by introducing a generic algorithmic framework that is
based on supervised machine learning (SML). First and foremost, this class of algorithms, referred to as texture
model registration (TMR), aims at making 2D/3D registration applicable for time-critical image guided medical
procedures. TMR methods are two-stage. In a first offline pre-computational stage, a prediction rule is derived
from a pre-interventional 3D image and according geometric constraints. This is achieved by computing digitally
reconstructed radiographs, pre-processing them, extracting their texture, and applying SML methods. In a
second online stage, the inferred rule is used for predicting the spatial rigid transformation of unseen intrainterventional
2D images. A first simple concrete TMR implementation, referred to as TMR-PCR, is introduced.
This approach involves principal component regression (PCR) and simple intermediate pre-processing steps.
Using TMR-PCR, first experimental results on five clinical IGRT 3D data sets and synthetic intra-interventional
images are presented. The implementation showed an average registration rate of 48 Hz over 40000 registrations,
and succeeded in the majority of cases with a mean target registration error smaller than 2 mm. Finally, the
potential and characteristics of the proposed methodical framework are discussed.
Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established
criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited
to 2D while it has been shown that proper 3D assessment of both the shape and the Bone Mineral Density
(BMD) distribution improves the fracture risk estimation. In this work we present a method to reconstruct both
the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image. A statistical
model of shape and a separate statistical model of the BMD distribution were automatically constructed from
a set of Quantitative Computed Tomography (QCT) scans. The reconstruction method incorporates a fully
automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally
reconstructed radiograph of the combined model. For the construction of the models, an in vitro dataset of
QCT scans of 60 anatomical specimens was used. To evaluate the reconstruction accuracy, experiments were
performed on simulated DXA images from the QCT scans of 30 anatomical specimens. Comparisons between
the reconstructions and the same subject QCT scans showed a mean shape accuracy of 1.2mm, and a mean
density error of 81mg/cm3. The results show that this method is capable of accurately reconstructing both the
3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially
improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost.
Using an atlas, an image can be segmented by mapping its coordinate space to that of the atlas in an anatomically correct
way. In order to find the correct mapping between the two different coordinate spaces e.g. diffeomorphic demons
registration can be applied. The demons algorithm is a popular choice for deformable image registration and offers the
possibility to perform computationally efficient non-rigid (diffeomorphic) registration. However, this registration method
is prone to image artifacts and image noise. Therefore it has been the main objective of the presented work to combine
the efficiency of diffeomorphic demons and the stability of statistical models. In the presented approach a statistical
deformation model that describes "anatomically correct" displacements vector fields for a specific registration problem is
used to guide the demons registration algorithm. By projecting the current displacement vector field, which is calculated
during any iteration of the registration process, into the model space a regularized version of the vector field can be
computed. Using this regularized vector field for the update of the deformation field in the subsequent iteration of the
registration process the demons registration algorithm can be guided by the deformation model. The proposed method
was evaluated on 21 CT datasets of the right hip. Measuring the average and maximum segmentation error for all 21
datasets and all 120 test configurations it could be demonstrated that the newly proposed algorithm leads to a reduction
of the segmentation error of up to 13% compared to using the conventional diffeomorphic demons algorithm.
Nowadays clinical diagnostic techniques like e.g. dual-energy X-ray absorptiometry are used to quantify bone quality.
However, bone mineral density alone is not sufficient to predict biomechanical properties like the fracture load for an
individual patient. Therefore, the development of tools, which can assess the bone quality in order to predicting
individual biomechanics of a bone, would mean a significant improvement for the prevention of fractures. In this paper
an approach to predict the fracture load of proximal femora by using a statistical appearance model will be presented. For
this purpose, 96 CT-datasets of anatomical specimen of human femora are used to create statistical models for the
prediction of the individual fracture load. Calculating statistical appearance models in different regions of interest by
using principal component analysis (PCA) makes it possible to use geometric as well as structural information about the
By regressing the output of PCA against the individual fracture load of 96 femora multi-linear regression models using a
leave-one-out cross validation scheme have been created. The resulting correlations are comparable to studies that partly
use higher image resolutions.
In this paper we present a new approach for the registration of cardiac 4D image sequences of different subjects,
where we assume that a temporal association between the sequences is given. Moreover, we allow for one (or two)
selected pair(s) of associated points in time of both sequences, which we call the bridging points in time, the use
of additional information such as the semi-automatic segmentation of the investigated structure. We establish
the 3D inter-subject registration for all other pairs of points in time exploiting (1) the inter-subject registration
for the bridging pair of points in time, (2) the intra-subject motion calculation in both sequences with respect
to the bridging pair, and (3) the concatenation of the obtained transformations. We formulate a cost functional
integrating the similarity measures comparing the images of the bridging pair(s) of points in time and of the
current pair of points in time, respectively. We evaluated our algorithm on 8 healthy volunteers leading to 28
inter-subject combinations and we analyze the behaviour for different parameter settings weighting differently the involved pairs of points in time. The approach based on the bridging pairs outperforms a direct 3D registration of corresponding points in time, in particular in the right ventricle we gain up to 33% in registration accuracy. Starting with a cost functional taking into account the similarity at the first bridging point in time, the results improve stepwise by integrating, firstly, information from the current pair of points in time and secondly, from a second bridging point in time. Our results also show a steep rise of the importance of regularization on the registration accuracy when registering the current point in time with our procedure (17% gain in accuracy) with respect to a direct registration in the bridging point (less than 1%). However, regularization during intra-sequence registration had only minor effects on the accuracy of our registration procedure.
The surgical treatment of femur fractures, which often result from osteoporosis, is highly dependent on the quality of the femoral bone. Unsatisfying results of surgical interventions like early loosening of implants may be one result of altered bone quality. However, clinical diagnostic techniques to quantify local bone quality are limited and often highly observer dependent. Therefore, the development of tools, which automatically and reproducibly place regions of interest (ROI) and asses the local quality of the femoral bone in these ROIs would be of great help for clinicians.
For this purpose, a method to position and deform ROIs automatically and reproducibly depending on the size and shape of the femur will be presented. Moreover, an approach to asses the femur quality, which is based on calculating texture features using co-occurrence matrices and these adaptive regions, will be proposed.
For testing purposes, 15 CT-datasets of anatomical specimen of human femora are used. The correlation between the texture features and biomechanical properties of the proximal femoral bone is calculated. First results are very promising and show high correlation between the calculated features and biomechanical properties. Testing the method on a larger data pool and refining the algorithms to further increase its sensitivity for altered bone quality will be the next steps in this project.
The endovascular repair of an abdominal aortic aneurysm is a minimal
invasive therapy which has been established during the past 15 years. A stent-graft is placed inside the aorta in order to cover the weakened regions of its wall. During a time interval of one or
more years the stent-graft can migrate and deform with the risk of
the occlusion of one of its limbs or of the rupture of the aneurysm.
In this work we developed several strategies to quantify the
migration and deformation in order to assess the risk coming with
these movements and especially to characterize appearing
complications by them. We calculated the rigid movement of the
stent-graft and the aorta relative to the spinal canal. For this
purpose, firstly, we rigidly registered the spinal canals, extracted
for the different points in time, in order to establish a fixed
reference system. All objects have been segmented first and surface
points have been determined before applying a rigid and non-rigid
point set registration algorithm. The change in the residual error
after registration of the stent-graft with an increasing number
of degrees of freedom indicates the amount of change in the
stent-graft's morphology. We investigated a sample of 9. Two cases could be clearly
distinguished by the quantified parameters: a high global migration
and a strong reduction of the residual error after non-rigid
registration. In both cases, strong complications have been
detected by the examination of clinical experts but only by means of the
images acquired one year later.
In this work a software platform for semiautomatic segmentation of medical images based on geometric deformable models will be presented. Including filters for image preprocessing, image segmentation and 3D visualization this toolkit offers the possibility of creating highly effective segmentation pipelines by combining classic segmentation techniques like seeded region growing and manual segmentation with modern level set segmentation algorithms. By individually combining input and output of different segmentation methods, specific and at the same time easy to use segmentation pipelines can be created. Using open source libraries for the implementation of a number of frequently used preprocessing and segmentation algorithms allowed effective programming by at the same time providing stable and highly effective algorithms. The usage of modern programming standards and developing cross-platform algorithm classes guarantees extensibility and flexible implementation in different hard- and software settings. Segmentation results, created in different research projects will be presented and the efficient usage of this framework will be demonstrated. The implementation of parts of the framework in a clinical setting is in progress and currently we are working on the embedding of statistical models and prior knowledge in the segmentation framework.